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Title: Branch Prediction with Multilayer Neural Networks: The Value of Specialization
Abstract—Multi-layer neural networks show promise in im- proving branch prediction accuracy. Tarsa et al. have shown that convolutional neural networks (CNNs) can accurately predict many branches that state-of-the-art branch predictors cannot. Yet, strict latency and storage constraints make naive adoption of typical neural network architectures impractical. Thus, it is necessary to understand the unique characteristics of branch prediction to design constraint-aware neural networks. This paper studies why CNNs are so effective for two hard-to- predict branches from the SPEC benchmark suite. We identify custom prediction algorithms for these branches that are more accurate and cost-efficient than CNNs. Finally, we discuss why out-of-the-box machine learning techniques do not find optimal solutions and propose research directions aimed at solving these inefficiencies.  more » « less
Award ID(s):
2011145
PAR ID:
10249272
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Machine Learning for Computer Architecture and Systems
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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